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Crane plays a very important role in national economy with greatly reduced labor intensity, improved production efficiency and promoted social development as an indispensable auxiliary tool and process equipment. Therefore, its energy consumption becomes an unavoidable topic and in fact, energy consumption of crane is very huge. It has been proved to be the most cost-effective way for reducing energy consumption to establish and implement new energy efficiency standard. Thus, it is necessary to analyze and evaluate the energy efficiency for overhead crane so as to propose a new energy efficiency standard. The paper aims to discuss these issues.

In this paper, four kinds of energy consumption sources of overhead crane is considered, based on which, an energy efficiency grading model for overhead crane based on BP neural network is proposed. Second, DS evidential theory is analyzed and based on it, an energy efficiency evaluation model based on BP neural network and DS evidential theory is proposed. The evaluation procedure is discussed in detail. Then, a case is demonstrated how the evaluation is carried out.

If overhead cranes with different energy consumptions need to be graded according to energy efficiency, the criterions to establish the energy efficiency labels for overhead cranes is proposed in this paper.

The research results can provide energy efficiency standard proposal of overhead crane for relative departments to monitor the design, manufacturing and use of overhead crane.

An energy efficiency grading model for overhead crane based on BP neural network is proposed. An energy efficiency evaluation model based on BP neural network and DS evidential theory is proposed.

Welding process is a complicated process influenced by many interference factors, a single sensor cannot get information describing welding process roundly. This paper simultaneously uses different sensors to get different information about the welding process, and uses multi‐sensor information fusion technology to fuse the different information. By using multi‐sensors, this paper aims to describe the welding process more precisely.

Electronic and welding pool image information are, respectively, obtained by arc sensor and image sensor, then electronic signal processing and image processing algorithms are used to extract the features of the signals, the features are then fused by neural network to predict the backside width of weld pool.

Comparative experiments show that the multi‐sensor fusion technology can predict the weld pool backside width more precisely.

The multi‐sensor fusion technology is used to fuse the different information obtained by different sensors in a gas tungsten arc welding process. This method gives a new approach to obtaining information and describing the welding process.

Global trade depends on more complex, prolonged and larger port systems, where port logistics is a backbone for such operations. Ports are responsible for transferring more than 80 percent of the global trade. Port logistics are prone to being risk-oriented. The study proposes a model to study various port logistics barriers and their associated risks for emerging economies in the Industry 4.0 era.

The study develops a framework by integrating the fuzzy set theory, the evidential reasoning approach and the expected utility theorem for identifying the severity value of port logistics barriers under the Industry 4.0 era for emerging economies and prioritize them based on various perspectives. The study identifies multiple risks associated with the barriers, and intensity-based categorization of the risks is performed for risk profiling.

The study reveals that poor infrastructure, nonsupportive policy ecosystem, and lack of research and development are the top barriers that need immediate attention. A new approach has been proposed that changes the importance of perspectives, and 192 analytical experiments were done to study the changing behavior of barriers. The study also presents various types of risks associated with the selected barriers.

In future studies, other barriers can be discovered and studied to develop such models. To cover the entire spectrum of possibilities, belief degrees of the barriers could be used to study the barriers instead of changing the weights.

This study presents a quantification model to prioritize the barriers based on environmental, economic and operational perspectives. Further, the model helps create scenarios for decision-makers to improve port logistics performance and achieve sustainability. The study identifies various risks associated with port logistics barriers and allows decision-makers to take proactive actions.

This study contributes significantly to the literature on port logistics by developing a framework for determining the severity of the barriers in the Industry 4.0 era for emerging economies. Further, the study pinpoints various risks associated with port logistics, and risk profiling is carried out.

The status of welding process is difficult to monitor because of the intense disturbance during the process. The purpose of this paper is to use multiple sensors to obtain information about the process from different aspects and use multi‐sensor information fusion technology to fuse the information, to obtain more precise information about the process than using a single sensor alone.

Arc sensor, visual sensor, and sound sensor were used simultaneously to obtain weld current, weld voltage, weld pool's image, and weld sound about the pulsed gas tungsten‐arc welding (GTAW) process. Then special algorithms were used to extract the signal features of different information. Fuzzy measure and fuzzy integral method were used to fuse the extracted signal features to predict the penetration status about the welding process.

Experiment results show that fuzzy measure and fuzzy integral method can effectively utilize the information obtained by different sensors and obtain better prediction results than a single sensor.

Arc sensor, visual sensor, and sound sensor are used in pulsed GTAW at the same time to obtain information, and fuzzy measure and fuzzy integral method are used to fuse the different features in welding process for the first time; experiment results show that multi‐sensor information can obtain better results than single sensor, this provides a new method for monitoring welding status and to control the welding process more precisely.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

This paper aims to develop a comprehensive uncertainty quantification method using evidence theory for Park–Ang damage index-based performance design in which epistemic uncertainties are considered. Various sources of uncertainty emanating from the database of the cyclic test results of RC members provided by the Pacific Earthquake Engineering Research Center are taken into account.

In this paper, an uncertainty quantification methodology based on evidence theory is presented for the whole process of performance-based seismic design (PBSD), while considering uncertainty in the Park–Ang damage model. To alleviate the burden of high computational cost in propagating uncertainty, the differential evolution interval optimization strategy is used for efficiently finding the propagated belief structure throughout the whole design process.

The investigation results of this paper demonstrate that the uncertainty rooted in Park–Ang damage model have a significant influence on PBSD design and evaluation. It might be worth noting that the epistemic uncertainty present in the Park–Ang damage model needs to be considered to avoid underestimating the true uncertainty.

This paper presents an evidence theory-based uncertainty quantification framework for the whole process of PBSD.

This essay is a review of the recent literature on the methodology of economics, with a focus on three broad trends that have defined the core lines of research within the discipline during the last two decades. These trends are: (a) the philosophical analysis of economic modelling and economic explanation; (b) the epistemology of causal inference, evidence diversity and evidence-based policy and (c) the investigation of the methodological underpinnings and public policy implications of behavioural economics. The final output is inevitably not exhaustive, yet it aims at offering a fair taste of some of the most representative questions in the field on which many philosophers, methodologists and social scientists have recently been placing a great deal of intellectual effort. The topics and references compiled in this review should serve at least as safe introductions to some of the central research questions in the philosophy and methodology of economics.

This paper aims to explore the critical factors which affect the quality of preschool education service so that targeted and effective measures to improve service quality can be put forward.

Evidential theory is applied to aggregate experts’ knowledge, and a fuzzy cognitive map (FCM) model of preschool education service quality is established to further carry out a simulation for inference, thus figuring out the critical factors to improve service quality.

The simulation results show that the main body of supervision and environment of governments and policies are two critical factors affecting the quality of preschool education service. More emphasis should be put on these two aspects, and corresponding measures can be put forward so as to ensure the quality of preschool education service.

This paper proposes a new model based on FCM and evidential theory to study the factors affecting preschool education service quality.

The purpose of this study is to develop a taxonomy of the major factors that influence auditors' selection and assessment of evidential cues. A discussion of how future research can help extend the taxonomy is also offered.

A literature review of prior research on auditors' utilization of evidential cues is introduced, followed by a summary taxonomy of the variables influencing this decision. Then, an exploration of future research directions is introduced.

A four‐category taxonomy is designed. Auditors' decision to incorporate a certain piece of evidence is a function of one or more categories of factors: individual, environmental, task related, or related to the nature of the cues. With its emphasis on audit quality, and thus on enhanced evidence gathering, the Sarbanes‐Oxley Act (SOX) has the potential to alter and/or expand the structure of the proposed taxonomy.

The taxonomy is based mainly on prior research in accounting and auditing. Incorporating other research on evidence selection and utilization from human behavior and human psychology may enhance the model.

The paper integrates into a model a large body of research on evidential cue selection and use in auditing. The taxonomy is a convenient starting point for researchers attempting to locate prior research in this area. Additionally, the taxonomy is an important guideline for audit partners and managers when planning an audit for two reasons. First, it sheds the light on many variables audit partners and managers need to consider when assigning audit tasks to audit team members with various levels of expertise. Second, it explores the impact of the SOX on current evidence collection practices in auditing.

The Dempster‐Shafer theory of evidence gives a solid basis for reasoning about situations characterized by uncertainty. A key feature of the theory is that propositions are represented as subsets of a set which is called a hypothesis space. This power set along with the set operations is a Boolean algebra. The theory has previously been shown to cover other Boolean algebras including collections of objects such as propositions. The practical advantages of this generalization are that increased flexibility of representation is allowed and that the performance of evidence accumulation can be enhanced. The objects of interest here are geometric forms, and we can encode rectangular and other shaped forms using hexadecimal numbers according to shapes and positions. Boolean algebra of such shapes can then be used directly in evidential reasoning exercised. Discusses how medical and other fields can gain from this approach.